Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version — Hardware Requirements & GPU Compatibility
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Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version is a 32B-parameter open language model from AlicanKiraz0 in the QwQ family. At Q4_K_M it needs about 21.12 GB of VRAM — see which GPUs and Macs can run it below.
215 downloads
13 likesSpecifications
- Publisher
- AlicanKiraz0
- Family
- QwQ
- Parameters
- 32B
- Release Date
- 2025-03-15
- License
- MIT
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How Much VRAM Does Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 15.0 GB | — | 13.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 17.2 GB | — | 15.60 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 21.1 GB | — | 19.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 25.1 GB | — | 22.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 29.0 GB | — | 26.40 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 35.2 GB | — | 32.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 70.4 GB | — | 64.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
Q4_K_M · 21.1 GBSeneca Cybersecurity LLM X QwQ 32B Q4 Medium Version (Q4_K_M) requires 21.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
Q4_K_M · 21.1 GB41 devices with unified memory can run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version need?
Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version requires 21.1 GB of VRAM at Q4_K_M, or 70.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 32B × 4.8 bits ÷ 8 = 19.2 GB
KV Cache + Overhead ≈ 1.9 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M21.1 GB- Can NVIDIA GeForce RTX 4090 run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
Yes, at Q4_K_M (21.1 GB) or lower. Higher quantizations like Q5_K_M (25.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
For Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version, Q4_K_M (21.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (25.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.0 GB.
VRAM requirement by quantization
Q2_K15.0 GBQ4_K_M ★21.1 GBQ5_K_M25.1 GBQ6_K29.0 GBQ8_035.2 GBBF1670.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version on a Mac?
Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version requires at least 15.0 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version locally?
Yes — Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version can run locally on consumer hardware. At Q4_K_M quantization it needs 21.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
At Q4_K_M, Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version can reach ~208 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~31 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 21.1 × 0.65 = ~246 tok/s
Estimated speed at Q4_K_M (21.1 GB)
~246 tok/s~31 tok/s~246 tok/s~208 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
At Q4_K_M, the download is about 19.20 GB. The full-precision BF16 version is 64.00 GB. The smallest option (Q2_K) is 13.60 GB.
- Which GPUs can run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
7 consumer GPUs can run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version at Q4_K_M (21.1 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version?
41 devices with unified memory can run Seneca Cybersecurity LLM X QwQ 32B Q4 Medium Version at Q4_K_M (21.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.